Purpose Of The Study

The influx of immigrants entering the United States can be largely attributed, among other reasons, to the desire for economic, religious, or political freedom. Personally, as an immigrant from the Philippines, our family’s migration was driven by the potential upward socioeconomic mobility that could be fulfilled by higher-paying jobs in the United States. The culture shock experienced during the transition to a new country can be an agent for establishing closer family ties as a means to preserve one’s cultural identity. Because of this, the transition to college – an environment away from family – could prompt an immigrant student to keep frequent contact with his/her parents. However, for immigrant families, the pursuit to improve their socioeconomic status could demand longer work hours, which could negatively affect the family dynamic and reduce parent-child interactions. According Roubinov and Boyce, “parenting is strongly influenced by and situated within the larger social ecology in which it unfolds, including the socioeconomic context.” (Roubinov and Boyce, 2018)

A study conducted in 1999 determined that “jobs decrease parental free time available to the adolescent, and workplace stress may spill over into family life. (Baer, 1999)”

This study aims to look at the relationship between citizenship status and family cohesion and determine if socioeconomic status influences this relationship. The motivation for this study stems from my personal experience as my parents have instilled the importance of retaining cultural and family values. I want to explore if other students with immigrant backgrounds maintain strong family ties in college, or if they exhibit more independence away from family. Lastly, I want to explore if immigrant families are sacrificing their relationships with their children by continuously pursuing economic gains.

I hypothesize that college students with immigrant backgrounds maintain more frequent contact with their families than their non-immigrant counterparts. However, I also predict that immigrant families with higher socioeconomic status will experience diminishing frequency of contact from their children.

The Data Set

Data was collected from all ECON 220L Sections. 116 people responded to a series of questionnaires. For this specific study, the respondent’s Citizenship Status, Gross Annual Income, and the Call Time (Mins/Week) spent on talking to the student’s family on the phone.

# Loading dataset into R
load("~/ECON 220 Lab/Econ220DataF2019_ano.Rdata")

#Rename document
Prodata<- Econ220DataF2019_ano

# Data Cleanup: Question 23
# Are you born in the US?


# Data Cleanup: Question 18
# Are you an international student?


# Data Cleanup: Question 19
# How many minutes per week do you talk to your family on the phone?
Prodata$q19[Prodata$q19=="30 min"]<-30
Prodata$q19[Prodata$q19=="210 minutes"]<-210
Prodata$q19[Prodata$q19=="60 minutes"]<-60
Prodata$q19[Prodata$q19=="30min"]<-30
Prodata$q19 <- as.numeric(Prodata$q19,na.rm = TRUE)
Prodata$q19[Prodata$q19 == 1000.0]<-NA

# Data Cleanup: Question 232
# What do you think is your annual gross household income?
Prodata$q232[Prodata$q232=="20"]<-20000
Prodata$q232[Prodata$q232=="$500000"]<-500000
Prodata$q232[Prodata$q232=="10k"]<-10000
Prodata$q232[Prodata$q232=="$200000"]<-200000
Prodata$q232[Prodata$q232=="10k"]<-10000
Prodata$q232[Prodata$q232=="n/a"]<-NA
Prodata$q232[Prodata$q232=="don't know"]<-NA
Prodata$q232[Prodata$q232=="80,000$"]<-80000
Prodata$q232[Prodata$q232=="middle"]<-NA
Prodata$q232[Prodata$q232=="1000000 dollars"]<-1000000
Prodata$q232[Prodata$q232=="I dont know"]<-NA
Prodata$q232[Prodata$q232=="0"]<-NA
Prodata$q232[Prodata$q232=="$48,000"]<-48000
Prodata$q232[Prodata$q232 == 100] <- 100000
Prodata$q232 <- as.numeric(Prodata$q232,na.rm = TRUE)


#Creating Data Frame Based on Citizenship Status
DataFrame <- data.frame("Race"=Prodata$race, "Sex"=Prodata$sex, "U.S Born"=Prodata$q23, "International Student"=Prodata$q18, "Call Minutes/Week"=Prodata$q19, "Gross Annual Income"=Prodata$q232)

DataFrame$Sex <- as.factor(DataFrame$Sex)
DataFrame$Race <- as.factor(DataFrame$Race)

USborn <- subset(DataFrame, U.S.Born == TRUE,  select = c("Race","Sex","Call.Minutes.Week","Gross.Annual.Income"))
USborn$Citizenship <- "U.S. Born"

International <- subset(DataFrame, International.Student == TRUE, select = c("Race","Sex","Call.Minutes.Week","Gross.Annual.Income"))
International$Citizenship <- "International"

Immigrant <- subset(DataFrame,U.S.Born == FALSE & International.Student == FALSE, select = c("Race","Sex","Call.Minutes.Week","Gross.Annual.Income"))
Immigrant$Citizenship <- "Immigrant"


citizenshipdf <- rbind(USborn, International, Immigrant)
citizenshipdf2<- citizenshipdf[c("Race", "Sex", "Citizenship","Call.Minutes.Week", "Gross.Annual.Income")]

colnames(citizenshipdf2)<- c("Race", "Sex", "Citizenship", "Call Time (Mins/Week)", "Gross Annual Income")

citizenshipdf2$`Call Time (Mins/Week)`<- as.numeric(citizenshipdf2$`Call Time (Mins/Week)`)
citizenshipdf2$`Gross Annual Income` <- as.numeric(citizenshipdf2$`Gross Annual Income`)

popnoNA<-subset(citizenshipdf2,!is.na(citizenshipdf2$'Gross Annual Income' & citizenshipdf2$'Call Time (Mins/Week)'))

pt<-tabyl(popnoNA$Citizenship, sort = TRUE)
colnames(pt)<- c("Citizenship Status", "Frequency (n=65)","%")
kable(pt,align="c") %>% kable_styling("striped", "bordered")

summtab<- citizenshipdf2 %>% select(Citizenship, `Call Time (Mins/Week)`, `Gross Annual Income`) %>% na.omit() %>% group_by(Citizenship)  %>% summarise_all(funs(mean, min, quantile(., 0.25) , median, quantile(., 0.75), max), na.rm=TRUE)


colnames(summtab) <- c("Citizenship", "Mean Call Time (Min/Week)", "Mean Gross Annual Income", "Min Call Time (Min/Week)", "Min Gross Annual Income", "Q1 Call Time (Min/Week)", "Q1 Gross Annual Income", "Median Call Time (Min/Week)", "Median Gross Annual Income", "Q3 Call Time (Min/Week)", "Q3 Gross Annual Income", "Max Call Time (Min/Week)", "Max Gross Annual Income")

df_transpose = t(summtab)
colnames(df_transpose) <- df_transpose[1, ]
df_transpose <- df_transpose[-1, ]

l1<-c("Mean Call Time (Min/Week)", "Min Call Time (Min/Week)", "Q1 Call Time (Min/Week)", "Median Call Time (Min/Week)", "Q3 Call Time (Min/Week)", "Max Call Time (Min/Week)")
kab1<-subset(df_transpose, rownames(df_transpose) %in% l1)
row.names(kab1)<- c("Mean", "Min", "Q1", "Median","Q3", "Max")
kable(kab1,align="c") %>% kable_styling("striped", "bordered") %>% add_header_above(c("", "Citizenship Status" = 3))

l2<-c("Mean Gross Annual Income", "Min Gross Annual Income", "Q1 Gross Annual Income", "Median Gross Annual Income", "Q3 Gross Annual Income" , "Max Gross Annual Income")
kab1<-subset(df_transpose, rownames(df_transpose) %in% l2)
row.names(kab1)<- c("Mean", "Min", "Q1","Median","Q3", "Max")
kable(kab1,align="c") %>% kable_styling("striped", "bordered") %>% add_header_above(c("", "Citizenship Status" = 3))

Data Analysis

After reviewing the raw data, outliers were removed to establish clearer trends within the data set. The distribution of Gross Annual Income and Call Time for different citizenship status were each visualized through box plots and histograms. A scatterplot mapping the 3 variables aforementioned shows if there are any positive or negative trends associated with the data.

immigrant_subset<-subset(popnoNA, Citizenship=="Immigrant")
USBorn_subset<-subset(popnoNA, Citizenship=="U.S. Born")
international_subset<-subset(popnoNA, Citizenship=="International")

# Removing Outliers based on 1.5IQR rule
popnoNA$`Gross Annual Income`[popnoNA$`Gross Annual Income` == 6000000]<- NA
popnoNA$`Gross Annual Income`[popnoNA$`Gross Annual Income` == 1000000]<- NA
popnoNA$`Gross Annual Income`[popnoNA$`Gross Annual Income` == 1500000]<- NA
popnoNA$`Gross Annual Income`[popnoNA$`Gross Annual Income` == 700000]<- NA
popnoNA$`Gross Annual Income`[popnoNA$`Gross Annual Income` == 600000]<- NA
immigrant_subset$`Gross Annual Income`[immigrant_subset$`Gross Annual Income` == 6000000]<- NA
immigrant_subset$`Gross Annual Income`[immigrant_subset$`Gross Annual Income` == 1000000]<- NA
international_subset$`Gross Annual Income`[international_subset$`Gross Annual Income` == 1000000]<- NA
international_subset$`Gross Annual Income`[international_subset$`Gross Annual Income` == 1500000]<- NA
international_subset$`Gross Annual Income`[international_subset$`Gross Annual Income` == 700000]<- NA
USBorn_subset$`Gross Annual Income`[USBorn_subset$`Gross Annual Income` == 700000]<- NA
USBorn_subset$`Gross Annual Income`[USBorn_subset$`Gross Annual Income` == 500000]<- NA
USBorn_subset$`Gross Annual Income`[USBorn_subset$`Gross Annual Income` == 450000]<- NA
popnoNA$`Call Time (Mins/Week)`[popnoNA$`Call Time (Mins/Week)` == 240]<- NA
popnoNA$`Call Time (Mins/Week)`[popnoNA$`Call Time (Mins/Week)` == 100]<- NA
popnoNA$`Call Time (Mins/Week)`[popnoNA$`Call Time (Mins/Week)` == 600]<- NA
popnoNA$`Call Time (Mins/Week)`[popnoNA$`Call Time (Mins/Week)` == 140]<- NA
USBorn_subset$`Call Time (Mins/Week)`[USBorn_subset$`Call Time (Mins/Week)` == 600] <- NA
international_subset$`Call Time (Mins/Week)`[international_subset$`Call Time (Mins/Week)` == 600] <- NA

bp1<- ggplot(data=popnoNA, aes(x=Citizenship,  y=`Gross Annual Income` , fill=Citizenship))+
  geom_boxplot( alpha=0.5)+
labs(title="Gross Annual Income in U.S. Dollars") +
   theme(plot.title = element_text(hjust = 0.5)) + scale_fill_manual(values=c("violetred4","pink4", "lightcoral"))

ggplotly(bp1)

dist_imm <- immigrant_subset %>% select(`Gross Annual Income`) %>%  na.omit() %>% ggplot(mapping=aes(x= `Gross Annual Income`)) +
  geom_histogram(aes(y =..density.., fill=..count..), breaks=seq(0, 500000, by = 50000), position= "identity", alpha=0.6, binwidth=0.1)  + geom_density(col="red4", alpha=0.2) + ggtitle("Income Distribution for Immigrant Students") + scale_fill_continuous(low="palevioletred1", high="red4")

ggplotly(dist_imm)

dist_int <- international_subset %>% select(`Gross Annual Income`) %>%  na.omit() %>% ggplot(mapping=aes(x= `Gross Annual Income`)) +
  geom_histogram(aes(y =..density.., fill=..count..), breaks=seq(-1, 500000, by = 50000), position= "identity", alpha=0.6, binwidth=0.1)  + geom_density(col="red4", alpha=0.2) + ggtitle("Income Distribution for International Students") + scale_fill_continuous(low="palevioletred1", high="red4")

ggplotly(dist_int)

dist_usb <- USBorn_subset %>% select(`Gross Annual Income`) %>%  na.omit() %>% ggplot(mapping=aes(x= `Gross Annual Income`)) +
  geom_histogram(aes(y =..density.., fill=..count..), breaks=seq(0, 400000, by = 50000), position= "identity", alpha=0.6, binwidth=0.1)  + geom_density(col="red4", alpha=0.2) + ggtitle("Income Distribution for U.S. Born Students") + scale_fill_continuous(low="palevioletred1", high="red4")
ggplotly(dist_usb)

bp<-ggplot(data=popnoNA, aes(x=Citizenship,  y=`Call Time (Mins/Week)` , fill=Citizenship))+
  geom_boxplot( alpha=0.5)+
labs(title="Call Time per Week in Minutes") +
   theme(plot.title = element_text(hjust = 0.5)) + scale_fill_manual(values=c("violetred4","pink4", "lightcoral"))
ggplotly(bp)

dist_imm1 <- immigrant_subset %>% select(`Call Time (Mins/Week)`) %>%  na.omit() %>% ggplot(mapping=aes(x= `Call Time (Mins/Week)`)) +
  geom_histogram(aes(y =..density.., fill=..count..), breaks=seq(0, 100, by = 20), position= "identity", alpha=0.6, binwidth=0.1)  + geom_density(col="red4", alpha=0.2) + ggtitle("Call Time Distribution for Immigrant Students") + scale_fill_continuous(low="palevioletred1", high="red4")
ggplotly(dist_imm1)

dist_int1 <- international_subset %>% select(`Call Time (Mins/Week)`) %>%  na.omit() %>% ggplot(mapping=aes(x= `Call Time (Mins/Week)`)) +
  geom_histogram(aes(y =..density.., fill=..count..), breaks=seq(0, 120, by = 20), position= "identity", alpha=0.6, binwidth=0.1)  + geom_density(col="red4", alpha=0.2) + ggtitle("Call Time Distribution for International Students") + scale_fill_continuous(low="palevioletred1", high="red4")
ggplotly(dist_int1)

dist_usb1 <- USBorn_subset %>% select(`Call Time (Mins/Week)`) %>%  na.omit() %>% ggplot(mapping=aes(x= `Call Time (Mins/Week)`)) +
  geom_histogram(aes(y =..density.., fill=..count..), breaks=seq(0, 120, by = 20), position= "identity", alpha=0.6, binwidth=0.1)  + geom_density(col="red4", alpha=0.2) + ggtitle("Call Time Distribution for U.S. Born Students") + scale_fill_continuous(low="palevioletred1", high="red4")
ggplotly(dist_usb1)

ggplot(data=popnoNA, mapping=aes(`Call Time (Mins/Week)`, `Gross Annual Income`, color=Citizenship)) +
  geom_point(alpha=0.5)+ scale_color_manual(values=c("violetred4","pink4", "lightcoral")) + 
  geom_smooth(method='lm') + ylim(0,600000) + labs(title="Gross Annual Income vs. Call Time") +  theme(plot.title = element_text(hjust = 0.5))

corvals <- popnoNA %>%  group_by(`Citizenship`) %>% na.omit %>% summarise(`Standard Deviation: Gross Annual Income` =sd(`Gross Annual Income`), `Standard Deviation: Call Time` =sd(`Call Time (Mins/Week)`),  `Variance: Gross Annual Income` = var(`Gross Annual Income`), `Variance: Call Time`= var(`Call Time (Mins/Week)`))

kable(corvals,align="c") %>%  kable_styling("striped", "bordered")

corvals1 <- popnoNA %>%  group_by(`Citizenship`) %>% na.omit %>% summarise(`Correlation Coefficient`=cor(`Call Time (Mins/Week)`, `Gross Annual Income`), `Covariance`= cov(`Call Time (Mins/Week)`, `Gross Annual Income`))

kable(corvals1,align="c") %>%  kable_styling("striped", "bordered") 

AvgValnoOutlier <- popnoNA %>% select(Citizenship, `Call Time (Mins/Week)`, `Gross Annual Income`) %>% na.omit() %>% group_by(Citizenship) %>% summarise(`Average Call Time (Mins/Week)`=round(mean(`Call Time (Mins/Week)`)), `Average Gross Annual Income` = round(mean(`Gross Annual Income`)))

AvgValnoOutlier$`Average Gross Annual Income`<-AvgValnoOutlier$`Average Gross Annual Income` / 1000

Avglong <- gather(AvgValnoOutlier, condition, measurement, `Average Call Time (Mins/Week)`:`Average Gross Annual Income`, factor_key=TRUE)

Avglong %>%
  group_by(condition, Citizenship) %>%
  ggplot(mapping=aes(x=Citizenship, y=measurement, fill=condition, label=measurement)) +
  geom_col()+
  geom_text(size=4, fontface=2, color="gray21", position = position_stack(vjust = 0.5))+
  ylab("Thousands of Dollars / Minutes") + xlab("Citizenship Status")+
  ggtitle("Average Values of Income and Call Time by Citizenship Status") +
  theme(plot.title = element_text(hjust = 0.5)) + scale_fill_manual(values=c("pink1", "lightpink3"))

data_test1 <- subset(popnoNA , popnoNA$Citizenship == "Immigrant" | popnoNA$Citizenship == "International")
data_test2 <- subset(popnoNA , popnoNA$Citizenship == "Immigrant" | popnoNA$Citizenship == "U.S. Born")
data_test3 <- subset(popnoNA , popnoNA$Citizenship == "International" | popnoNA$Citizenship == "U.S. Born")

t.test(`Call Time (Mins/Week)` ~ `Citizenship` ,data=data_test1)

## 
##  Welch Two Sample t-test
## 
## data:  Call Time (Mins/Week) by Citizenship
## t = -2.7, df = 23, p-value = 0.01
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -41.956  -5.649
## sample estimates:
##     mean in group Immigrant mean in group International 
##                       17.67                       41.47

t.test(`Call Time (Mins/Week)` ~ `Citizenship` ,data=data_test2)

## 
##  Welch Two Sample t-test
## 
## data:  Call Time (Mins/Week) by Citizenship
## t = -3, df = 25, p-value = 0.007
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -51.673  -9.309
## sample estimates:
## mean in group Immigrant mean in group U.S. Born 
##                   17.67                   48.16

t.test(`Call Time (Mins/Week)` ~ `Citizenship` ,data=data_test3)

## 
##  Welch Two Sample t-test
## 
## data:  Call Time (Mins/Week) by Citizenship
## t = -0.67, df = 37, p-value = 0.5
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -26.82  13.44
## sample estimates:
## mean in group International     mean in group U.S. Born 
##                       41.47                       48.16

t.test(`Gross Annual Income` ~ `Citizenship` ,data=data_test1)

## 
##  Welch Two Sample t-test
## 
## data:  Gross Annual Income by Citizenship
## t = 0.93, df = 8.5, p-value = 0.4
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -96075 227959
## sample estimates:
##     mean in group Immigrant mean in group International 
##                      216875                      150933

t.test(`Gross Annual Income` ~ `Citizenship` ,data=data_test2)

## 
##  Welch Two Sample t-test
## 
## data:  Gross Annual Income by Citizenship
## t = 1, df = 10, p-value = 0.3
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -87740 244546
## sample estimates:
## mean in group Immigrant mean in group U.S. Born 
##                  216875                  138472

t.test(`Gross Annual Income` ~ `Citizenship` ,data=data_test3)

## 
##  Welch Two Sample t-test
## 
## data:  Gross Annual Income by Citizenship
## t = 0.32, df = 37, p-value = 0.8
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -66351  91274
## sample estimates:
## mean in group International     mean in group U.S. Born 
##                      150933                      138472

Conclusion

Based on the data above, on average, U.S. Born students have the lowest average Gross Annual Income while Immigrant students have the highest average Gross Annual Income. In contrast, U.S. Born students have the largest average call time and Immigrants have the lowest average call time.

Additionally, according to the results of the scatterplot and the values for the correlation coefficient, Immigrant Students had the largest positive correlation (0.7217) between Call Time (Mins/Week) and Gross Annual Income. In other words, a rise in the immigrant student’s family Gross Annual Income is correlated to a longer time spent by that student talking to his/her parents on the phone, which could be an indicator of stronger parent-child interactions. This finding does not support my hypothesis as I claimed that a higher paying job could indicate longer work hours and reduced time to foster parent-child interactions. It is possible that cultural effects are in play and stronger bonds are formed in immigrant families, regardless of the pursuit for higher socioeconomic status.

Conversely, international students had the lowest positive correlation (0.1191) between Call Time (Mins/Week) and Gross Annual Income. This means that the increase in Gross Annual Income does not greatly affect the length of an international student’s call time. Since the international student group had the lowest average call time, it is possible that this value is not related to their parent’s income but is due to other factors such as the availability of long-distance calls or time difference between the United States and their home countries that would limit the times available for calling.

U.S. Born students were in the middle in terms of the positive correlation (0.2736) between Call Time and Gross Annual Income. However, this correlation coefficient is still low which could indicate that Call Time - and parent-child interactions, are not directly determined by the U.S. Born student’s income. This group actually had the largest average Call Time within the 3 groups, and thus a stronger parent-child relationship could be due to other factors such as being able to grow up under the constant supervision of parents (e.g. no separation occured with the families, as opposed to the experiences of international students and possibly the immigrant students as well). In general, trends in parent-child interactions are challenging to generalize due to the various unique life experiences, parenting styles, and different upbringings that individuals face on a case-by-case basis.

References

• Baer, J. “The effects of family structure and SES on family processes in early adolescence” Journal of adolescence. , 1999, Vol.22(3), p.341-354

• Barrett, A., Turner, R. “Family Structure and Mental Health: The Mediating Effects of Socioeconomic Status, Family Process, and Social Stress”. Journal of Health and Social Behavior, 2005, Vol. 46, Issue 2.

• Roubinov, D., Boyce, T. “Parenting and SES: relative values or enduring principles?”. Current Opinion in Psychology, 2017, Vol. 15, pp. 162-167.